Scalable spatiotemporal regression model based on Moran’s eigenvectors
Scalable spatiotemporal regression model based on Moran’s eigenvectors
复制标题
DOI：
10.1080/13658816.2022.2100891
复制
发表时间：
2022-07
影响因子：
5.7
通讯作者：
Hayato Nishi;Yasushi Asami;H. Baba;C. Shimizu
中科院分区：
文献类型：
--
作者：
Hayato Nishi;Yasushi Asami;H. Baba;C. Shimizu
摘要基于Moran的特征向量和高效的计算算法，我们提出了一个具有时空变系数的可伸缩回归模型。考虑时空非平稳性的回归模型很重要，因为许多真实世界的数据集，如房价，都与地理和时间位置有关。虽然地理加权回归(GWR)及其变体被广泛用于建模空间变化的系数，但它们不能处理大数据集。我们采用了一种基于Moran特征向量的空间变化系数的替代建模方法，并将其扩展到处理大型时空数据集。此外，我们还介绍了一种可扩展的学习算法，该算法利用了基于卡尔曼滤波和期望最大化算法的模型结构。即使对于GWR无法处理的大数据集，我们的可伸缩算法也是有效的。为了评估所提出的模型的性能，我们将其应用于在日本东京收集的住房市场数据集。结果表明，在提高计算速度的同时，该模型的预测性能与GWR相当。此外，较大的数据集可以加速算法的收敛。
Abstract We propose a scalable regression model with spatially and temporally varying coefficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and temporal locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation–maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.